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Topic modeling has become a cornerstone of natural language processing (NLP), enabling researchers to summarize and navigate massive document archives. This paper explores the transition from traditional probabilistic models to modern neural architectures.

: Models are typically assessed based on interpretability, stability, and efficiency . 071408apeamelbrnldn pdf

: The standard process includes corpus collection, preprocessing (e.g., creating a document-term-matrix), model estimation, and validation. Topic modeling has become a cornerstone of natural

: Integration of deep neural networks has led to Neural Topic Models (NTMs) , which facilitate complex tasks like text generation and summarization. creating a document-term-matrix)

1. Introduction